Every Model has its Place: Combining Systems, Data-driven, and Generative Models for Therapeutic Design

Drug discovery is a molecular search task with a challenging objective: modify the function of a complex biological system to interrupt disease processes. Conventionally, it is a costly, high failure-rate process – with molecular candidates clearing preclinical safety and efficacy hurdles only to fail upon delivery to humans. This happens partly because early screens in the discovery pipeline fall short of capturing the ultimate therapeutic value of new molecular candidates. For a molecule to become a successful drug, it should: (1) bind to a desired target protein; (2) be deliverable from a desired site of administration (oral, intravenous, etc.) to the physiological site of activity, with sufficient concentration for a sufficient duration of time; and (3) promote the desired pharmacological effect without causing unwanted toxicity. The chemical space that meets one of these objectives likely requires compromises in another, as binding, delivery, and activity depend on coupled and dynamic biophysical and biochemical interactions. To help improve the success rate of drug discovery, we should ideally look at design through the lens of human physiology. In this presentation, I will discuss our work on integrating mechanistic systems models with data-driven machine learning and generative AI models to empower physiology-informed design of potential therapeutics. For more information see: https://cbe.utk.edu/people/belinda-akpa/ *Contents* 00:00 - Introduction 06:08 - Every Model has its Place: Combining Systems, Data-driven, and Generative Models for Therapeutic Design 44:30 - Questions and Discussion Moderator: James A. Glazier, PhD, Indiana University, Bloomington If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/